Geisler, Simon

24 publications

TMLR 2025 Adversarial Robustness of Graph Transformers Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann

ICLR 2025 Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance Dominik Fuchsgruber, Tim Postuvan, Stephan Günnemann, Simon Geisler

ICML 2025 REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann

ICML 2025 The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger

ICMLW 2024 Attacking Large Language Models with Projected Gradient Descent Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann

ICMLW 2024 Relaxing Graph Transformers for Adversarial Attacks Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann

NeurIPS 2024 Spatio-Spectral Graph Neural Networks Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann

ICMLW 2024 Spatio-Spectral Graph Neural Networks Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann

NeurIPS 2023 Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

NeurIPSW 2023 On the Adversarial Robustness of Graph Contrastive Learning Methods Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Mazen Kamel, Simon Geisler, Stephan Günnemann

NeurIPSW 2023 Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks Ege Erdogan, Simon Geisler, Stephan Günnemann

ICLR 2023 Revisiting Robustness in Graph Machine Learning Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

CoRL 2023 Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel

ICML 2023 Transformers Meet Directed Graphs Simon Geisler, Yujia Li, Daniel J Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru

NeurIPS 2022 Are Defenses for Graph Neural Networks Robust? Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski

ICLR 2022 Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

ICLR 2022 Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann

NeurIPS 2022 Randomized Message-Interception Smoothing: Gray-Box Certificates for Graph Neural Networks Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann

NeurIPSW 2022 Revisiting Robustness in Graph Machine Learning Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

NeurIPSW 2022 Revisiting Robustness in Graph Machine Learning Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

ICML 2022 Winning the Lottery Ahead of Time: Efficient Early Network Pruning John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann

NeurIPS 2021 Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann

NeurIPS 2021 Robustness of Graph Neural Networks at Scale Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann

NeurIPS 2020 Reliable Graph Neural Networks via Robust Aggregation Simon Geisler, Daniel Zügner, Stephan Günnemann